Artificial intelligence is an integral part of data-driven organizations. However, one of the main challenges is to cultivate a new mindset and culture around working with AI-related
technologies and products. In this masterclass we focus on this issue and discuss strategies and experiences to enhance and optimize this vital aspect of the business.
Developing AI-literacy in the management teams
Working through common mental blocks, such as the fear of being replaced by AI in the near future. Cultural adaptation consideration.
Creating the right expectations around project management in AI context. Developing a risk assessment framework for AI projects and identifying low effort and high impact
projects.
Opportunities and risks of using “out of the box” AI tools.
AI regulations are steadily approaching on a global scale, particularly within the EU and the U.S.
Privacy regulators across the globe have already initiated enforcement measures.
How can organizations get ready for these changes?
What notable industry approaches have emerged thus far?
How business can adopt AI safely and securely
Common risks: dealing with third party providers, AI-generated compliance documents from clients, etc.
What is the regulatory approach by the government?
Developing a methodology of how to develop and enable AI sacurity risk control.
Identifying the key challenges and best practices for auditing AI systems that are responsible, accountable, transparent, and fair
Auditing impact of AI on human rights, such as freedom of expression, privacy, equality, and dignity
It’s critical to ensure that AI systems do what they’re supposed to do
Human-rights-based audits of AI systems, including generative AI, and their outcomes and implications
Across financial services, companies are using AI for a range of applications. These include automating operational processes, improving customer service, and enhancing risk
management.
HSBC is currently developing AI tools to improve the end-to-end Sales lifecycle, from automating workflows to providing more useful customer insights
We will discuss a few use cases we are exploring, likely to have corollaries across many industriesWe will close with a “best practice and lessons learned” discussion of relevant
risks, issues, and options for mitigation
Utilizing the LLMs capabilities to automate the customer feedback management
Starting from extracting entities to text summarization, question answering to personalized emails generation
How to accelerate the process on both company and customer side
Designing a robust and compact Generative AI application in a trusted way
· How ROSEN's data evaluation team benefits from a continuously learning AI and a strategy with human involvement.
· How to design an intelligent process that leverages the respective strengths of AI or humans.
· Architectural design of a process-driven AI system.
· How to keep an AI system from making mistakes while complying with regulations.
Reflecting on a Vista’s 15 year journey with end-2-end automation of customized
product creation
How analytics and self-service fostered a data-drive culture, and helped streamline
manufacturing processes
✔ — 1200 data streams processing different sensor data
✔ — 9.5B JSON event messages per day
✔ — 3000 active looker users querying data mesh
✔ — 1900 active analytics API clients
✔ — >200k analytics session per day
Reflecting on TOP3 analytical use cases around Predictive Maintenance, Shift
Reviews and Inventory Optimization
Global companies grapple with the dual challenge of balancing risk and opportunities
while evaluating the sustainability threat posed by AI’s substantial computing needs
against the benefits derived from insights into areas like transition plans, human capital,
and biodiversity.
How might we develop purpose-led technology that can help build trust and achieve
business objectives which are aligned with ESG (and wider organizational goals)?
How could we use “AI for good” to solve some of our “wicked” social problems, such as
those covered by the United Nations Sustainable Development Goals (SDGs)?
How might we develop purpose-led technology that can help build trust and achieve
business objectives which are aligned with ESG (and wider organizational goals)?
How could we use “AI for good” to solve some of our “wicked” social problems, such as
those covered by the United Nations Sustainable Development Goals (SDGs)?
1. CSR Integration:
Showcase how CSR initiatives seamlessly integrate into overall strategy for
sustainable business practices.
2. Impactful Programs:
Highlighting of specific CSR programs, emphasizing measurable positive outcomes in
the communities served.
3. Efficiency Through Automation:
Illustration how intelligent automation enhances efficiency and agility, contributing to
successful CSR efforts.
4. Data-Driven Decisions:
Emphasizing intelligent automation’s role in optimizing resource allocation and
maximizing positive outcomes through data-driven decisions in CSR initiatives.
5. Innovation in CSR:
Communicating our commitment to continuous improvement and innovation, adapting
to societal needs and embracing technological advancements.
EU’s incomplete response to ethical concerns in generative AI.
Companies struggling to grasp and enact EU ethical principles.
Disconnect between principle formulation and practical implementation.
Crucial need to bridge the gap between endorsing principles and taking action